Multi-platform assessment of transcriptional profiling technologies utilizing a precise probe mapping methodology.

BACKGROUND: The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed "transcript pattern". A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. RESULTS: In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80% fold-change concordance with the gold standard qRT-PCR (TaqMan). CONCLUSIONS: This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations.

Next-generation sequencing identifies the natural killer cell microRNA transcriptome.

Natural killer (NK) cells are innate lymphocytes important for early host defense against infectious pathogens and surveillance against malignant transformation. Resting murine NK cells regulate the translation of effector molecule mRNAs (e.g., granzyme B, GzmB) through unclear molecular mechanisms. MicroRNAs (miRNAs) are small noncoding RNAs that post-transcriptionally regulate the translation of their mRNA targets, and are therefore candidates for mediating this control process. While the expression and importance of miRNAs in T and B lymphocytes have been established, little is known about miRNAs in NK cells. Here, we used two next-generation sequencing (NGS) platforms to define the miRNA transcriptomes of resting and cytokine-activated primary murine NK cells, with confirmation by quantitative real-time PCR (qRT-PCR) and microarrays. We delineate a bioinformatics analysis pipeline that identified 302 known and 21 novel mature miRNAs from sequences obtained from NK cell small RNA libraries. These miRNAs are expressed over a broad range and exhibit isomiR complexity, and a subset is differentially expressed following cytokine activation. Using these miRNA NGS data, miR-223 was identified as a mature miRNA present in resting NK cells with decreased expression following cytokine activation. Furthermore, we demonstrate that miR-223 specifically targets the 3' untranslated region of murine GzmB in vitro, indicating that this miRNA may contribute to control of GzmB translation in resting NK cells. Thus, the sequenced NK cell miRNA transcriptome provides a valuable framework for further elucidation of miRNA expression and function in NK cell biology.

SnoRNA microarray analysis reveals changes in H/ACA and C/D RNA levels caused by dyskerin ablation in mouse liver.

snoRNAs (small nucleolar RNAs) are key components of snoRNP (small nucleolar ribonucleoprotein) particles involved in modifying specific residues of ribosomal and other RNAs by pseudouridylation (H/ACA snoRNAs) or methylation (C/D snoRNAs). They are encoded within the introns of host genes, which tend to be genes whose products are involved in ribosome biogenesis or function. Although snoRNPs are abundant, ubiquitous and their components highly conserved, information concerning their expression during development or how their expression is altered in diseased states is sparse. To facilitate these studies we have developed a snoRNA microarray platform for the analysis of the abundance of snoRNAs in different RNA samples. In the present study we show that the microarray is sensitive and specific for the detection of snoRNAs. A mouse snoRNA microarray was used to monitor changes in abundance of snoRNAs after ablation of dyskerin, an H/ACA snoRNA protein component, from mouse liver, which causes a decrease in ribosome production. H/ACA snoRNAs were decreased in abundance in these livers while, unexpectedly, C/D snoRNAs were increased. The increase in C/D snoRNAs corresponded with an increase in the abundance of the mRNAs transcribed from snoRNA host genes, suggesting the increase may be part of a cellular response to defective ribosome synthesis.

'Calling Cards' method for high-throughput identification of targets of yeast DNA-binding proteins.

We present a protocol for a novel method for identifying the targets of DNA-binding proteins in the genome of the yeast Saccharomyces cerevisiae. This is accomplished by engineering a DNA-binding protein so that it leaves behind in the genome a permanent mark -- a 'calling card' -- that provides a record of that protein's visit to that region of the genome. The calling card is the yeast Ty5 retrotransposon, whose integrase interacts with the Sir4 protein. If Sir4 is fused to a DNA-binding protein, it recruits the Ty5 integrase, which directs insertion of a Ty5 calling card into the genome. The calling card along with the flanking genomic DNA is harvested by inverse PCR and its genomic location is determined by hybridization of the product to a DNA microarray. This method provides a straightforward alternative to the 'ChIP-chip' method for determining the targets of DNA-binding proteins. This protocol takes approximately 2 weeks to complete.